Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …

Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …

Abstract Domain adaptive object re-ID aims to transfer the learned knowledge from the
labeled source domain to the unlabeled target domain to tackle the open-class re …

Cross-modality person re-identification is a challenging task due to large cross-modality
discrepancy and intra-modality variations. Currently, most existing methods focus on …

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative
features without true labels. This paper formulates unsupervised person ReID as a multi …

Thanks to the recent research development in contrastive learning, the gap of visual
representation learning between supervised and unsupervised approaches has been …

Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by
measuring the feature similarity without considering the distribution discrepancy among …

We present a new framework for semantic segmentation without annotations via clustering.
Off-the-shelf clustering methods are limited to curated, single-label, and object-centric …